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영문 논문지

홈 홈 > 연구문헌 > 영문 논문지 > JIPS (한국정보처리학회)

JIPS (한국정보처리학회)

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한글제목(Korean Title) Contribution to Improve Database Classification Algorithms for Multi-Database Mining
영문제목(English Title) Contribution to Improve Database Classification Algorithms for Multi-Database Mining
저자(Author) Salim Miloudi   Sid Ahmed Rahal   Salim Khiat                       
원문수록처(Citation) VOL 14 NO. 03 PP. 0709 ~ 0726 (2018. 06)
한글내용
(Korean Abstract)
영문내용
(English Abstract)
Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to (n2–n)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.
키워드(Keyword) Connected Components   Database Classification   Graph-Based Algorithm   Multi-Database Mining                    
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